CN112818868B - Method and device for identifying illegal user based on behavior sequence characteristic data - Google Patents
Method and device for identifying illegal user based on behavior sequence characteristic data Download PDFInfo
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Abstract
The application relates to a method and a device for identifying illegal users based on behavior sequence characteristic data. The method comprises the following steps: detecting a resource transfer application, and acquiring equipment information and position information corresponding to the resource transfer application. And clustering the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications. And extracting user behavior data corresponding to each user category, carrying out sequence quantization processing based on the user behavior data, generating corresponding behavior sequence feature data, and further carrying out illegal user identification according to the behavior sequence feature data. By adopting the method, the illegal behaviors missed by the strong correlation map network can be cleaned, the efficiency and accuracy of checking the illegal cases are improved, the prevention and control means are enriched, the recognition accuracy of illegal users and illegal behaviors can be improved, and the enterprise loss is reduced.
Description
Technical Field
The application relates to the technical field of Internet, in particular to a method and a device for identifying illegal users based on behavior sequence characteristic data.
Background
With the development of internet technology and the gradual popularization and application of internet financial services, more and more people choose to transact various services through the internet, and the services can comprise different financial services such as fund loans or fund product purchases. However, some consumers have a greater demand for funds borrowing, but do not have corresponding revenue and loan compensation capabilities, which can easily result in a higher overdue risk for the bill. To avoid poor loans, businesses typically choose to reject the applied value units and loan requirements of such poor qualified users.
Under the background that part of users have higher lending demands and weaker repayment capacity, malicious financial intermediaries are induced, the intermediaries are good at packaging fake user personal information, a reasonable operation terminal is provided, users with poor qualification can pass through the risk management and control requirements of different enterprises, high-risk users can obtain credit approval and borrowing of corresponding enterprises, and the financial intermediaries can charge high commission fees from the intermediaries, so that economic and reputation losses are brought to the enterprises.
Conventionally, in order to reduce the problem of the proliferation of high-risk users caused by financial intermediaries, a correlation map technology is mostly adopted to build a network prevention and control system based on rich data and strong correlation dimensions, so as to identify and monitor users suspected to the financial intermediaries. For example, when a financial broker applies for a financial product to multiple users through the same mobile phone device, the mobile phone device serves as a strong medium, and multiple users are directly associated with each other through the same mobile phone device. And further, the users can be timely managed and controlled through a strong correlation map network.
However, the strong association network is only used, and the financial intermediaries with gradually changed means and corresponding illegal actions cannot be identified, for example, when the financial intermediaries attract users to designated places through a certain means and guide the users to apply for financial products on respective mobile phone devices, the identification accuracy of the traditional strong association network is still to be improved because the users use their mobile phone devices to apply for the financial products, and the members lose the strong medium of the mobile phone devices.
Disclosure of Invention
Based on the above, it is necessary to provide a method and a device for identifying an offending user based on behavior sequence feature data, which can improve accuracy of identifying an offending user and an offending behavior.
A method of offending user identification based on behavioral sequence feature data, the method comprising:
detecting a resource transfer application, and acquiring equipment information and position information corresponding to the resource transfer application;
Clustering the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications;
extracting user behavior data corresponding to each user category;
performing sequence quantization processing based on the user behavior data to generate corresponding behavior sequence feature data;
and carrying out illegal user identification according to the behavior sequence characteristic data.
In one embodiment, the clustering processing is performed on the users corresponding to each resource transfer application according to the location information and the device information to obtain different user classifications, including:
determining distance information between users corresponding to the resource transfer application according to the position information and the equipment information;
and clustering the users corresponding to each resource transfer application according to the distance information to obtain different user classifications.
In one embodiment, the performing a sequence quantization process based on the user behavior data to generate corresponding behavior sequence feature data includes:
And carrying out sequence quantization processing on the user behavior data according to the trained cascade recognition model to obtain behavior sequence characteristic data corresponding to the user behavior data.
In one embodiment, the identifying the offending user according to the behavior sequence feature data includes:
extracting corresponding behavior sequence characteristics from the behavior sequence characteristic data;
And performing similar behavior calculation based on the behavior sequence characteristics to generate an illegal user identification result.
In one embodiment, the performing similar behavior calculation based on the behavior sequence features, and generating the offending user identification result includes:
calculating according to the behavior sequence characteristics to obtain the behavior sequence similarity of the same user classification;
Performing secondary grouping on the users under the same user classification according to the behavior sequence similarity to obtain updated user classification;
and acquiring the updated user number under the user classification, comparing the user number with a preset user number threshold value, and generating an illegal user identification result.
In one embodiment, the method further comprises:
and carrying out real-time monitoring and tracking investigation on the illegal user according to a preset management and control logic.
An offending user identification device based on behavioral sequence characteristic data, the device comprising:
The resource transfer application detection module is used for detecting a resource transfer application and acquiring equipment information and position information corresponding to the resource transfer application;
The user classification module is used for carrying out clustering processing on the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications;
the user behavior data extraction module is used for extracting user behavior data corresponding to each user category;
The behavior sequence characteristic data generation module is used for carrying out sequence quantization processing based on the user behavior data to generate corresponding behavior sequence characteristic data;
And the illegal user identification module is used for identifying the illegal user according to the behavior sequence characteristic data.
In one embodiment, the user classification module is further configured to:
Determining distance information between users corresponding to the resource transfer application according to the position information and the equipment information; and clustering the users corresponding to each resource transfer application according to the distance information to obtain different user classifications.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
detecting a resource transfer application, and acquiring equipment information and position information corresponding to the resource transfer application;
Clustering the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications;
extracting user behavior data corresponding to each user category;
performing sequence quantization processing based on the user behavior data to generate corresponding behavior sequence feature data;
and carrying out illegal user identification according to the behavior sequence characteristic data.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
detecting a resource transfer application, and acquiring equipment information and position information corresponding to the resource transfer application;
Clustering the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications;
extracting user behavior data corresponding to each user category;
performing sequence quantization processing based on the user behavior data to generate corresponding behavior sequence feature data;
and carrying out illegal user identification according to the behavior sequence characteristic data.
In the method and the device for identifying the illegal user based on the behavior sequence characteristic data, the resource transfer application is detected, and the equipment information and the position information corresponding to the resource transfer application are acquired. And clustering the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications. And extracting user behavior data corresponding to each user category, carrying out sequence quantization processing based on the user behavior data, generating corresponding behavior sequence feature data, and further carrying out illegal user identification according to the behavior sequence feature data. The method is not limited to the traditional strong correlation network for controlling, but clustering is carried out according to the position information and the equipment information of the users, further clustering is carried out according to the behavior sequence characteristic data classified by each user, and then illegal user identification is carried out, so that illegal behaviors missed by the strong correlation map network can be cleaned, the efficiency and the accuracy of illegal case detection are improved, prevention and control means are enriched, the identification accuracy of the illegal users and the illegal behaviors can be improved, and enterprise loss is reduced.
Drawings
FIG. 1 is a diagram of an application environment for a method of offending user identification based on behavior sequence feature data in one embodiment;
FIG. 2 is a flow diagram of a method of offending user identification based on behavioral sequence characterization data in one embodiment;
FIG. 3 is a flow chart illustrating the identification of offending users based on behavior sequence feature data in one embodiment;
FIG. 4 is a flow chart of a method of identifying offending users based on behavior sequence feature data in another embodiment;
FIG. 5 is a block diagram of an offending user identification device based on behavioral sequence characterization data in one embodiment;
Fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The method for identifying the illegal user based on the behavior sequence characteristic data can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The server 104 detects a resource transfer application initiated by a user at the terminal 102, and obtains device information and location information corresponding to the resource transfer application, so as to perform clustering processing on the users corresponding to each resource transfer application according to the location information and the device information, thereby obtaining different user classifications. And extracting user behavior data corresponding to each user category, carrying out sequence quantization processing based on the user behavior data, generating corresponding behavior sequence feature data, and further carrying out illegal user identification according to the behavior sequence feature data. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 104 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, there is provided a method for identifying offending users based on behavior sequence feature data, which is described by taking the application of the method to the server in fig. 1 as an example, and includes the following steps:
step S202, detecting a resource transfer application, and acquiring equipment information and position information corresponding to the resource transfer application.
Specifically, a user initiates a resource transfer application at a terminal device, wherein the resource transfer application carries device information of the terminal device where the user initiating the application is located, and a geographic position, namely position information, where the user is hidden. Meanwhile, the user performs the key operation, the click operation and the content input operation on the content page initiating the resource transfer application, so as to apply for loans, and further, according to the key operation, the click operation and the content input operation performed on the content page, the user behavior data corresponding to the user can be produced.
The device information is used for identifying terminal devices where new users are located, judging whether a plurality of users use the same terminal devices to register or apply loans, the position information is used for determining the current geographic positions where the users are located, and judging whether the plurality of users are located at the same geographic positions at the same time or whether the intervals of the geographic positions where the plurality of users are located are larger than a preset distance threshold value.
And step S204, carrying out clustering processing on the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications.
Specifically, distance information between users corresponding to the resource transfer applications is determined according to the position information and the equipment information, and the users corresponding to the resource transfer applications are clustered according to the distance information to obtain different user classifications.
Further, according to the location information and the equipment information of the users initiating the resource transfer application, clustering is carried out on the users corresponding to each resource transfer application, namely, the users of different equipment information are clustered according to the distance between the geographic positions, and a plurality of users with the distance between the geographic positions smaller than a preset distance threshold are classified into the same user class.
Step S206, extracting user behavior data corresponding to each user category.
Specifically, by extracting user behavior data of each user under the same user category, wherein the user behavior data corresponding to each user can be produced according to key operation, click operation and content input operation performed by each user on a content page.
The user behavior data should not be limited to the operation data obtained by the application program embedded point and the sequence of clicking the embedded point by the user, and may include behavior data obtained by other ways and other manners, for example: user movement tracks when the user starts positioning, and behavior data with a certain sequence of characteristics, which are generated by the user.
Step S208, performing sequence quantization processing based on the user behavior data to generate corresponding behavior sequence feature data.
Specifically, according to the trained cascade recognition model, performing sequence quantization processing on the user behavior data to obtain behavior sequence feature data corresponding to the user behavior data.
The trained cascade recognition model is a cascade recognition model combining a long-short-period memory network model and a cyclic neural network model, and user behavior data such as user operation behaviors, page clicking sequences and sliding tracks are recognized and subjected to sequence quantization by combining the long-short-period memory network model and the cyclic neural network model which are suitable for processing time sequence problems.
Further, according to the trained cascade recognition model, namely the cascade recognition model combining the long-term memory network model and the cyclic neural network model, the user behavior data is subjected to sequence quantization processing, the user behavior sequence is quantized into digital features, and behavior sequence feature data corresponding to the user behavior data is generated.
Step S210, according to the behavior sequence characteristic data, illegal user identification is carried out.
Specifically, the corresponding behavior sequence characteristics are extracted from the behavior sequence characteristic data, similar behavior calculation is performed based on the behavior sequence characteristics, and an illegal user identification result is generated.
Further, according to the behavior sequence characteristics, the behavior sequence similarity of the same user classification is obtained through calculation, and according to the behavior sequence similarity, secondary clustering is carried out on all users under the same user classification, and updated user classification is obtained. And comparing the number of users with a preset user number threshold value to generate an illegal user identification result.
And when the number of users under the updated user classification is greater than a preset user number threshold, determining that each user under the user classification is an illegal user.
In one embodiment, after the offending user identification based on the behavior sequence characteristic data, further comprising:
and carrying out real-time monitoring and tracking investigation on the illegal user according to the preset management and control logic.
Specifically, according to preset management and control logic, real-time monitoring is conducted on the determined illegal users, terminal equipment, geographic positions and the like of the illegal users are determined, tracking investigation is further conducted, the illegal users are excluded from the service range, application of the illegal users is refused, and enterprise loss is reduced.
In the method for identifying the illegal user based on the behavior sequence characteristic data, the equipment information and the position information corresponding to the resource transfer application are obtained by detecting the resource transfer application. And clustering the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications. And extracting user behavior data corresponding to each user category, carrying out sequence quantization processing based on the user behavior data, generating corresponding behavior sequence feature data, and further carrying out illegal user identification according to the behavior sequence feature data. The method is not limited to the traditional strong correlation network for controlling, but clustering is carried out according to the position information and the equipment information of the users, further clustering is carried out according to the behavior sequence characteristic data classified by each user, and then illegal user identification is carried out, so that illegal behaviors missed by the strong correlation map network can be cleaned, the efficiency and the accuracy of illegal case detection are improved, prevention and control means are enriched, the identification accuracy of the illegal users and the illegal behaviors can be improved, and enterprise loss is reduced.
In one embodiment, as shown in fig. 3, the step of identifying the offending user according to the behavior sequence feature data specifically includes:
Step S302, extracting corresponding behavior sequence characteristics from the behavior sequence characteristic data.
Specifically, from the behavior sequence feature data, behavior sequence features of each user in the same user category may be extracted. The behavior sequence features comprise the sequence of behavior operations performed by a user when the user applies for resource transfer on a content page, wherein the behavior operations can comprise key operations, clicking operations, content input operations and the like. It will be appreciated that the behavior sequence characteristics may include data such as the order of key operations triggered by the user on the page, the order of click operations, and a sliding track.
The method comprises the steps of constructing a user behavior sequence characteristic database for storing updated behavior sequence characteristic data corresponding to user behavior data in real time.
Step S304, calculating according to the behavior sequence characteristics to obtain the behavior sequence similarity of the same user classification.
Specifically, based on the behavior sequence characteristics, the similarity of the behavior sequences among the users under the same user classification is calculated, namely, the similarity of the users under the same user classification in the sequence of the behavior operations performed when the resource transfer application is performed on the content page is calculated.
Among them, the behavior operation may include a key operation, a click operation, a content input operation, and the like. It can be understood that the behavior sequence similarity can be the similarity of data such as the sequence of key operations triggered by different users on a page, the sequence of clicking operations, and a sliding track.
And step S306, performing secondary grouping on each user under the same user classification according to the similarity of the behavior sequences to obtain updated user classifications.
Specifically, according to the similarity of the behavior sequences, performing secondary classification on each user under the same user classification, namely, performing secondary classification on the users with the similarity of the behavior sequences larger than a preset similarity threshold value, and obtaining updated user classification.
The similarity threshold is preset by an enterprise, can be adjusted, is not limited to specific values, and can have different values.
Step S308, the number of users under the updated user classification is obtained, and the number of users is compared with a preset user threshold value, so that an illegal user identification result is generated.
Specifically, the user number under the user classification is determined as the illegal user when the user number is determined to be greater than the preset user number threshold value by acquiring the updated user number under the user classification, and the preset user number threshold value is acquired, and real-time monitoring and tracking investigation are performed on the illegal user according to preset management and control logic.
And when the user number is determined to be smaller than the preset user number threshold value, each user under the user classification is determined to be a normal user. The preset user number threshold is preset by an enterprise, can be adjusted, is not limited to specific values, and can have different values.
In this embodiment, the behavior sequence similarity of the same user class is obtained by extracting corresponding behavior sequence features from the behavior sequence feature data and calculating according to the behavior sequence features, and then, according to the behavior sequence similarity, each user under the same user class is subjected to secondary clustering to obtain the updated user class. And comparing the number of users with a preset user number threshold value to generate an illegal user identification result. The method is not limited to the traditional strong correlation network management and control, but performs secondary clustering according to the behavior sequence characteristic data of each user classification on the basis of clustering according to the position information and the equipment information of the user, and performs illegal user identification, so that illegal behaviors missed by the strong correlation map network can be cleaned, the identification accuracy of the illegal users and the illegal behaviors is improved, and the enterprise loss is reduced.
In one embodiment, as shown in fig. 4, a method for identifying an offending user based on behavior sequence feature data is provided, which specifically includes the following steps:
1) Detecting a resource transfer application, and acquiring equipment information and position information corresponding to the resource transfer application.
2) And determining distance information between the users corresponding to the resource transfer application according to the position information and the equipment information.
3) And clustering the users corresponding to each resource transfer application according to the distance information to obtain different user classifications.
4) And extracting user behavior data corresponding to each user category.
5) And carrying out sequence quantization processing on the user behavior data according to the trained cascade recognition model to obtain behavior sequence characteristic data corresponding to the user behavior data.
6) Constructing a user behavior sequence characteristic database, and storing updated behavior sequence characteristic data corresponding to the user behavior data in real time.
7) And extracting corresponding behavior sequence characteristics from the behavior sequence characteristic data stored in the behavior sequence characteristic database.
8) And calculating according to the behavior sequence characteristics to obtain the behavior sequence similarity of the same user classification.
9) And performing secondary grouping on each user under the same user classification according to the similarity of the behavior sequences to obtain updated user classifications.
10 The updated user number under the user classification is obtained, and the user number is compared with a preset user number threshold value, so that an illegal user identification result is generated.
11 According to the preset control logic, real-time monitoring and tracking investigation are carried out on the illegal user.
In the method for identifying the illegal user based on the behavior sequence characteristic data, the equipment information and the position information corresponding to the resource transfer application are obtained by detecting the resource transfer application. And clustering the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications. And extracting user behavior data corresponding to each user category, carrying out sequence quantization processing based on the user behavior data, generating corresponding behavior sequence feature data, and further carrying out illegal user identification according to the behavior sequence feature data. The method is not limited to the traditional strong correlation network for controlling, but clustering is carried out according to the position information and the equipment information of the users, further clustering is carried out according to the behavior sequence characteristic data classified by each user, and then illegal user identification is carried out, so that illegal behaviors missed by the strong correlation map network can be cleaned, the efficiency and the accuracy of illegal case detection are improved, prevention and control means are enriched, the identification accuracy of the illegal users and the illegal behaviors can be improved, and enterprise loss is reduced.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a part of the steps in the flowcharts related to the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages performed is not necessarily sequential, but may be performed alternately or alternately with at least a part of the steps or stages in other steps or other steps.
In one embodiment, as shown in fig. 5, there is provided an offending user identification device based on behavior sequence feature data, including: a resource transfer application detection module 502, a user classification module 504, a user behavior data extraction module 506, a behavior sequence feature data generation module 508, and an offending user identification module 510, wherein:
the resource transfer application detection module 502 is configured to detect a resource transfer application, and obtain device information and location information corresponding to the resource transfer application.
And the user classification module 504 is configured to perform clustering processing on users corresponding to each resource transfer application according to the location information and the device information, so as to obtain different user classifications.
The user behavior data extraction module 506 is configured to extract user behavior data corresponding to each user category.
The behavior sequence feature data generating module 508 is configured to perform sequence quantization processing based on the user behavior data, and generate corresponding behavior sequence feature data.
And the offending user identification module 510 is configured to identify an offending user according to the behavior sequence feature data.
In the illegal user identification device based on the behavior sequence characteristic data, the equipment information and the position information corresponding to the resource transfer application are obtained by detecting the resource transfer application. And clustering the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications. And extracting user behavior data corresponding to each user category, carrying out sequence quantization processing based on the user behavior data, generating corresponding behavior sequence feature data, and further carrying out illegal user identification according to the behavior sequence feature data. The method is not limited to the traditional strong correlation network for controlling, but clustering is carried out according to the position information and the equipment information of the users, further clustering is carried out according to the behavior sequence characteristic data classified by each user, and then illegal user identification is carried out, so that illegal behaviors missed by the strong correlation map network can be cleaned, the efficiency and the accuracy of illegal case detection are improved, prevention and control means are enriched, the identification accuracy of the illegal users and the illegal behaviors can be improved, and enterprise loss is reduced.
In one embodiment, the offending user identification module is further configured to:
extracting corresponding behavior sequence characteristics from the behavior sequence characteristic data; calculating according to the behavior sequence characteristics to obtain the behavior sequence similarity of the same user classification; performing secondary grouping on each user under the same user classification according to the similarity of the behavior sequences to obtain updated user classifications; and acquiring the number of users under the updated user classification, comparing the number of users with a preset user number threshold value, and generating an illegal user identification result.
In the above-mentioned illegal user identification module, the behavior sequence similarity of the same user classification is obtained by extracting the corresponding behavior sequence features from the behavior sequence feature data and calculating according to the behavior sequence features, and then each user under the same user classification is subjected to secondary clustering according to the behavior sequence similarity, so as to obtain the updated user classification. And comparing the number of users with a preset user number threshold value to generate an illegal user identification result. The method is not limited to the traditional strong correlation network management and control, but performs secondary clustering according to the behavior sequence characteristic data of each user classification on the basis of clustering according to the position information and the equipment information of the user, and performs illegal user identification, so that illegal behaviors missed by the strong correlation map network can be cleaned, the identification accuracy of the illegal users and the illegal behaviors is improved, and the enterprise loss is reduced.
In one embodiment, the user classification module is further configured to:
Determining distance information between users corresponding to the resource transfer application according to the position information and the equipment information; and clustering the users corresponding to each resource transfer application according to the distance information to obtain different user classifications.
In one embodiment, the behavior sequence characteristic data generation module is further configured to:
And carrying out sequence quantization processing on the user behavior data according to the trained cascade recognition model to obtain behavior sequence characteristic data corresponding to the user behavior data.
In one embodiment, there is provided an offending user identification device based on behavior sequence feature data, further including an offending user monitoring module configured to:
and carrying out real-time monitoring and tracking investigation on the illegal user according to the preset management and control logic.
For specific limitations on the offending user identification device based on the behavior sequence feature data, reference may be made to the above limitation on the offending user identification method based on the behavior sequence feature data, and the description thereof will not be repeated here. The respective modules in the above-described offending user identification device based on behavior sequence feature data may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store device information, location information, and user behavior data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method for identifying offending users based on behavioral sequence characteristic data.
It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
Detecting a resource transfer application, and acquiring equipment information and position information corresponding to the resource transfer application;
Clustering the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications;
extracting user behavior data corresponding to each user category;
performing sequence quantization processing based on the user behavior data to generate corresponding behavior sequence characteristic data;
And carrying out illegal user identification according to the behavior sequence characteristic data.
In one embodiment, the processor when executing the computer program further performs the steps of:
determining distance information between users corresponding to the resource transfer application according to the position information and the equipment information;
And clustering the users corresponding to each resource transfer application according to the distance information to obtain different user classifications.
In one embodiment, the processor when executing the computer program further performs the steps of:
And carrying out sequence quantization processing on the user behavior data according to the trained cascade recognition model to obtain behavior sequence characteristic data corresponding to the user behavior data.
In one embodiment, the processor when executing the computer program further performs the steps of:
Extracting corresponding behavior sequence characteristics from the behavior sequence characteristic data;
And performing similar behavior calculation based on the behavior sequence characteristics to generate an illegal user identification result.
In one embodiment, the processor when executing the computer program further performs the steps of:
calculating according to the behavior sequence characteristics to obtain the behavior sequence similarity of the same user classification;
Performing secondary grouping on each user under the same user classification according to the similarity of the behavior sequences to obtain updated user classifications;
and acquiring the number of users under the updated user classification, comparing the number of users with a preset user number threshold value, and generating an illegal user identification result.
In one embodiment, the processor when executing the computer program further performs the steps of:
and carrying out real-time monitoring and tracking investigation on the illegal user according to the preset management and control logic.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
Detecting a resource transfer application, and acquiring equipment information and position information corresponding to the resource transfer application;
Clustering the users corresponding to each resource transfer application according to the position information and the equipment information to obtain different user classifications;
extracting user behavior data corresponding to each user category;
performing sequence quantization processing based on the user behavior data to generate corresponding behavior sequence characteristic data;
And carrying out illegal user identification according to the behavior sequence characteristic data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining distance information between users corresponding to the resource transfer application according to the position information and the equipment information;
And clustering the users corresponding to each resource transfer application according to the distance information to obtain different user classifications.
In one embodiment, the computer program when executed by the processor further performs the steps of:
And carrying out sequence quantization processing on the user behavior data according to the trained cascade recognition model to obtain behavior sequence characteristic data corresponding to the user behavior data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Extracting corresponding behavior sequence characteristics from the behavior sequence characteristic data;
And performing similar behavior calculation based on the behavior sequence characteristics to generate an illegal user identification result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
calculating according to the behavior sequence characteristics to obtain the behavior sequence similarity of the same user classification;
Performing secondary grouping on each user under the same user classification according to the similarity of the behavior sequences to obtain updated user classifications;
and acquiring the number of users under the updated user classification, comparing the number of users with a preset user number threshold value, and generating an illegal user identification result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and carrying out real-time monitoring and tracking investigation on the illegal user according to the preset management and control logic.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method for identifying offending users based on behavioral sequence feature data, the method comprising:
Detecting a resource transfer application, and acquiring equipment information and position information corresponding to the resource transfer application; the device information is the device information of the terminal device where the user initiating the resource transfer application is located, and the location information is the current geographic location of the user;
Determining distance information between users corresponding to the resource transfer applications according to the position information and the equipment information, and carrying out clustering processing on the users corresponding to the resource transfer applications according to the distance information to obtain different user classifications;
Extracting user behavior data corresponding to each user category; the user behavior data comprise user operation behaviors, page clicking sequences and sliding tracks;
performing sequence quantization processing based on the user behavior data, quantizing a user behavior sequence into digital characteristics, and generating behavior sequence characteristic data corresponding to the user behavior data; the behavior sequence characteristic data are obtained by identifying and sequence quantifying the user operation behaviors, the page clicking sequence and the sliding track;
And extracting corresponding behavior sequence characteristics from the behavior sequence characteristic data, performing similar behavior calculation based on the behavior sequence characteristics, and generating an illegal user identification result.
2. The method of claim 1, wherein the performing a sequence quantization process based on the user behavior data, quantizing a user behavior sequence to a digital feature, generating behavior sequence feature data corresponding to the user behavior data, comprises:
and according to the trained cascade recognition model, carrying out sequence quantization processing on the user behavior data, and quantizing the user behavior sequence into digital characteristics to obtain behavior sequence characteristic data corresponding to the user behavior data.
3. The method of claim 1, wherein the performing similar behavior calculations based on the behavior sequence features to generate offending user identification results comprises:
calculating according to the behavior sequence characteristics to obtain the behavior sequence similarity of the same user classification;
Performing secondary grouping on the users under the same user classification according to the behavior sequence similarity to obtain updated user classification;
and acquiring the updated user number under the user classification, comparing the user number with a preset user number threshold value, and generating an illegal user identification result.
4. A method according to any one of claims 1 to 3, characterized in that the method further comprises:
and carrying out real-time monitoring and tracking investigation on the illegal user according to a preset management and control logic.
5. An offending user identification device based on behavioral sequence characteristic data, the device comprising:
The resource transfer application detection module is used for detecting a resource transfer application and acquiring equipment information and position information corresponding to the resource transfer application; the equipment information is the equipment information of the terminal equipment where the user initiating the resource transfer application is located, and the position information is the current geographic position where the user is located; the user classification module is used for determining distance information among the users corresponding to the resource transfer applications according to the position information and the equipment information, and carrying out clustering processing on the users corresponding to the resource transfer applications according to the distance information to obtain different user classifications;
the user behavior data extraction module is used for extracting user behavior data corresponding to each user category; the user behavior data comprise user operation behaviors, page clicking sequences and sliding tracks;
The behavior sequence feature data generation module is used for carrying out sequence quantization processing based on the user behavior data, quantizing the user behavior sequence into digital features and generating behavior sequence feature data corresponding to the user behavior data; the behavior sequence characteristic data are obtained by identifying and sequence quantifying the user operation behaviors, the page clicking sequence and the sliding track;
And the illegal user identification module is used for extracting corresponding behavior sequence characteristics from the behavior sequence characteristic data, performing similar behavior calculation based on the behavior sequence characteristics and generating an illegal user identification result.
6. The apparatus of claim 5, wherein the behavior sequence characteristic data generation module is further configured to:
and according to the trained cascade recognition model, carrying out sequence quantization processing on the user behavior data, and quantizing the user behavior sequence into digital characteristics to obtain behavior sequence characteristic data corresponding to the user behavior data.
7. The apparatus of claim 5, wherein the offending user identification module is further configured to:
Extracting corresponding behavior sequence characteristics from the behavior sequence characteristic data; calculating according to the behavior sequence characteristics to obtain the behavior sequence similarity of the same user classification; performing secondary grouping on the users under the same user classification according to the behavior sequence similarity to obtain updated user classification; and acquiring the updated user number under the user classification, comparing the user number with a preset user number threshold value, and generating an illegal user identification result.
8. The apparatus of any one of claims 5 to 7, further comprising an offending user monitoring module configured to:
and carrying out real-time monitoring and tracking investigation on the illegal user according to a preset management and control logic.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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